{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用随机森林算法API对乳腺癌数据进行分类操作，根据特征属性预测是否会得宫颈癌的四个目标属性的值，并理解随机森林中决策树数量和决策树深度对模型的影响"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [RandomForestClassifier 随机森林](http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier)\n",
    "- [roc_curve ROC曲线](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Number of sexual partners</th>\n",
       "      <th>First sexual intercourse</th>\n",
       "      <th>Num of pregnancies</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Smokes (years)</th>\n",
       "      <th>Smokes (packs/year)</th>\n",
       "      <th>Hormonal Contraceptives</th>\n",
       "      <th>Hormonal Contraceptives (years)</th>\n",
       "      <th>IUD</th>\n",
       "      <th>...</th>\n",
       "      <th>STDs: Time since first diagnosis</th>\n",
       "      <th>STDs: Time since last diagnosis</th>\n",
       "      <th>Dx:Cancer</th>\n",
       "      <th>Dx:CIN</th>\n",
       "      <th>Dx:HPV</th>\n",
       "      <th>Dx</th>\n",
       "      <th>Hinselmann</th>\n",
       "      <th>Schiller</th>\n",
       "      <th>Citology</th>\n",
       "      <th>Biopsy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Number of sexual partners First sexual intercourse Num of pregnancies  \\\n",
       "0   18                       4.0                     15.0                1.0   \n",
       "1   15                       1.0                     14.0                1.0   \n",
       "\n",
       "  Smokes Smokes (years) Smokes (packs/year) Hormonal Contraceptives  \\\n",
       "0    0.0            0.0                 0.0                     0.0   \n",
       "1    0.0            0.0                 0.0                     0.0   \n",
       "\n",
       "  Hormonal Contraceptives (years)  IUD  ...    \\\n",
       "0                             0.0  0.0  ...     \n",
       "1                             0.0  0.0  ...     \n",
       "\n",
       "  STDs: Time since first diagnosis STDs: Time since last diagnosis Dx:Cancer  \\\n",
       "0                                ?                               ?         0   \n",
       "1                                ?                               ?         0   \n",
       "\n",
       "  Dx:CIN Dx:HPV Dx Hinselmann Schiller Citology Biopsy  \n",
       "0      0      0  0          0        0        0      0  \n",
       "1      0      0  0          0        0        0      0  \n",
       "\n",
       "[2 rows x 36 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [u'Age', u'Number of sexual partners', u'First sexual intercourse',\n",
    "       u'Num of pregnancies', u'Smokes', u'Smokes (years)',\n",
    "       u'Smokes (packs/year)', u'Hormonal Contraceptives',\n",
    "       u'Hormonal Contraceptives (years)', u'IUD', u'IUD (years)', u'STDs',\n",
    "       u'STDs (number)', u'STDs:condylomatosis',\n",
    "       u'STDs:cervical condylomatosis', u'STDs:vaginal condylomatosis',\n",
    "       u'STDs:vulvo-perineal condylomatosis', u'STDs:syphilis',\n",
    "       u'STDs:pelvic inflammatory disease', u'STDs:genital herpes',\n",
    "       u'STDs:molluscum contagiosum', u'STDs:AIDS', u'STDs:HIV',\n",
    "       u'STDs:Hepatitis B', u'STDs:HPV', u'STDs: Number of diagnosis',\n",
    "       u'STDs: Time since first diagnosis', u'STDs: Time since last diagnosis',\n",
    "       u'Dx:Cancer', u'Dx:CIN', u'Dx:HPV', u'Dx', u'Hinselmann', u'Schiller',\n",
    "       u'Citology', u'Biopsy']#df.columns\n",
    "path = \"datas/risk_factors_cervical_cancer.csv\"  # 数据文件路径\n",
    "data = pd.read_csv(path)\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Number of sexual partners</th>\n",
       "      <th>First sexual intercourse</th>\n",
       "      <th>Num of pregnancies</th>\n",
       "      <th>Smokes</th>\n",
       "      <th>Smokes (years)</th>\n",
       "      <th>Smokes (packs/year)</th>\n",
       "      <th>Hormonal Contraceptives</th>\n",
       "      <th>Hormonal Contraceptives (years)</th>\n",
       "      <th>IUD</th>\n",
       "      <th>...</th>\n",
       "      <th>STDs:HIV</th>\n",
       "      <th>STDs:Hepatitis B</th>\n",
       "      <th>STDs:HPV</th>\n",
       "      <th>STDs: Number of diagnosis</th>\n",
       "      <th>STDs: Time since first diagnosis</th>\n",
       "      <th>STDs: Time since last diagnosis</th>\n",
       "      <th>Dx:Cancer</th>\n",
       "      <th>Dx:CIN</th>\n",
       "      <th>Dx:HPV</th>\n",
       "      <th>Dx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
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       "      <td>?</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Number of sexual partners First sexual intercourse Num of pregnancies  \\\n",
       "0   18                       4.0                     15.0                1.0   \n",
       "\n",
       "  Smokes Smokes (years) Smokes (packs/year) Hormonal Contraceptives  \\\n",
       "0    0.0            0.0                 0.0                     0.0   \n",
       "\n",
       "  Hormonal Contraceptives (years)  IUD ... STDs:HIV STDs:Hepatitis B STDs:HPV  \\\n",
       "0                             0.0  0.0 ...      0.0              0.0      0.0   \n",
       "\n",
       "  STDs: Number of diagnosis STDs: Time since first diagnosis  \\\n",
       "0                         0                                ?   \n",
       "\n",
       "  STDs: Time since last diagnosis Dx:Cancer Dx:CIN Dx:HPV Dx  \n",
       "0                               ?         0      0      0  0  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data[names[0:-4]]\n",
    "Y = data[names[-4:]]\n",
    "X.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 18.        ,   4.        ,  15.        ,   1.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
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       "         0.        ,   0.        ,   6.14084507,   5.81690141,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#空值的处理\n",
    "X = X.replace(\"?\", np.NAN)\n",
    "imputer = Imputer(missing_values=\"NaN\")#使用Imputer给定缺省值，默认的是以mean\n",
    "X = imputer.fit_transform(X, Y)\n",
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练样本数量:686,特征属性数目:32,目标属性数目:4\n",
      "测试样本数量:172\n"
     ]
    }
   ],
   "source": [
    "#数据分割\n",
    "x_train,x_test,y_train,y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
    "print (\"训练样本数量:%d,特征属性数目:%d,目标属性数目:%d\" % (x_train.shape[0],x_train.shape[1],y_train.shape[1]))\n",
    "print (\"测试样本数量:%d\" % x_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#标准化\n",
    "ss = MinMaxScaler()\n",
    "x_train = ss.fit_transform(x_train, y_train)\n",
    "x_test = ss.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#降维\n",
    "pca = PCA(n_components=2)\n",
    "x_train = pca.fit_transform(x_train)\n",
    "x_test = pca.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=1, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=100, n_jobs=1, oob_score=False, random_state=0,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#随机森林模型\n",
    "# n_estimators 森林里的树木数量\n",
    "# max_depth 不宜设置过大，把每一个模型当做弱分类器\n",
    "forest = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=1, random_state=0)\n",
    "forest.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Hinselmann  Schiller  Citology  Biopsy\n",
      "196           0         0         0       0\n",
      "645           0         0         0       0\n",
      "122           0         0         0       0\n",
      "852           0         0         0       0\n",
      "167           0         0         0       0\n",
      "278           0         0         0       0\n",
      "346           0         0         0       0\n",
      "239           0         0         0       0\n",
      "10            0         0         0       0\n",
      "484           0         0         0       0\n",
      "68            0         0         0       0\n",
      "375           0         0         0       0\n",
      "432           0         0         0       0\n",
      "681           0         0         0       0\n",
      "231           0         0         0       0\n",
      "386           0         0         1       0\n",
      "406           0         0         0       0\n",
      "214           1         1         0       1\n",
      "2             0         0         0       0\n",
      "849           0         0         0       0\n",
      "487           0         0         0       0\n",
      "494           0         0         0       0\n",
      "694           0         0         0       0\n",
      "622           0         0         0       0\n",
      "154           0         0         0       0\n",
      "803           0         0         0       0\n",
      "791           0         0         0       0\n",
      "585           0         0         0       0\n",
      "279           0         0         0       0\n",
      "471           0         0         0       0\n",
      "..          ...       ...       ...     ...\n",
      "115           0         0         0       0\n",
      "777           0         0         0       0\n",
      "72            0         0         0       0\n",
      "537           0         0         0       0\n",
      "677           0         0         0       0\n",
      "841           0         0         0       0\n",
      "174           0         0         0       0\n",
      "87            0         0         0       0\n",
      "551           0         0         0       0\n",
      "486           0         0         0       0\n",
      "705           0         0         0       0\n",
      "314           1         1         0       1\n",
      "396           0         0         0       0\n",
      "600           0         0         0       0\n",
      "472           1         1         0       1\n",
      "70            0         0         0       0\n",
      "599           0         0         0       0\n",
      "804           0         0         0       0\n",
      "754           0         1         0       1\n",
      "277           0         0         0       0\n",
      "723           0         0         0       0\n",
      "9             0         0         0       0\n",
      "359           0         0         0       0\n",
      "707           0         0         0       0\n",
      "763           0         0         0       0\n",
      "835           0         0         0       0\n",
      "192           0         0         0       0\n",
      "629           0         0         0       0\n",
      "559           0         0         0       0\n",
      "684           0         0         0       0\n",
      "\n",
      "[686 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率:89.53%\n",
      "Hinselmann目标属性AUC值： 0.990197404002\n",
      "Schiller目标属性AUC值： 0.955922120065\n",
      "Citology目标属性AUC值： 0.963797998918\n",
      "Biopsy目标属性AUC值： 0.95686857761\n"
     ]
    }
   ],
   "source": [
    "#模型效果评估\n",
    "score = forest.score(x_test, y_test)\n",
    "print (\"准确率:%.2f%%\" % (score * 100))\n",
    "#模型预测 预测X的类概率\n",
    "forest_y_score = forest.predict_proba(x_test)\n",
    "# roc_curve curve曲线的意思 即计算ROC值\n",
    "forest_fpr1, forest_tpr1, _ = metrics.roc_curve(label_binarize(y_test[names[-4]],classes=(0,1,2)).T[0:-1].T.ravel(), forest_y_score[0].ravel())\n",
    "forest_fpr2, forest_tpr2, _ = metrics.roc_curve(label_binarize(y_test[names[-3]],classes=(0,1,2)).T[0:-1].T.ravel(), forest_y_score[1].ravel())\n",
    "forest_fpr3, forest_tpr3, _ = metrics.roc_curve(label_binarize(y_test[names[-2]],classes=(0,1,2)).T[0:-1].T.ravel(), forest_y_score[2].ravel())\n",
    "forest_fpr4, forest_tpr4, _ = metrics.roc_curve(label_binarize(y_test[names[-1]],classes=(0,1,2)).T[0:-1].T.ravel(), forest_y_score[3].ravel())\n",
    "#AUC值\n",
    "auc1 = metrics.auc(forest_fpr1, forest_tpr1)\n",
    "auc2 = metrics.auc(forest_fpr2, forest_tpr2)\n",
    "auc3 = metrics.auc(forest_fpr3, forest_tpr3)\n",
    "auc4 = metrics.auc(forest_fpr4, forest_tpr4)\n",
    "\n",
    "print (\"Hinselmann目标属性AUC值：\", auc1)\n",
    "print (\"Schiller目标属性AUC值：\", auc2)\n",
    "print (\"Citology目标属性AUC值：\", auc3)\n",
    "print (\"Biopsy目标属性AUC值：\", auc4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172\n",
      "[[ 0.94411699  0.05588301]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.91911229  0.08088771]\n",
      " [ 0.77698808  0.22301192]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.93961695  0.06038305]\n",
      " [ 0.94358933  0.05641067]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.91503202  0.08496798]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.91503202  0.08496798]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.93838456  0.06161544]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.86832487  0.13167513]\n",
      " [ 0.77698808  0.22301192]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94358933  0.05641067]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.93838456  0.06161544]\n",
      " [ 0.94362753  0.05637247]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94362753  0.05637247]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.84528497  0.15471503]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.88126188  0.11873812]\n",
      " [ 0.86099614  0.13900386]\n",
      " [ 0.94362753  0.05637247]\n",
      " [ 0.94362753  0.05637247]\n",
      " [ 0.91794396  0.08205604]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94362753  0.05637247]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.88355612  0.11644388]\n",
      " [ 0.89127619  0.10872381]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.93239584  0.06760416]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.86099614  0.13900386]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.93961695  0.06038305]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.91794396  0.08205604]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.91911229  0.08088771]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.94358933  0.05641067]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.86099614  0.13900386]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.89127619  0.10872381]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.91794396  0.08205604]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.94362753  0.05637247]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.84776216  0.15223784]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.9408745   0.0591255 ]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.91911229  0.08088771]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94362753  0.05637247]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.94337781  0.05662219]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.93961695  0.06038305]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94496063  0.05503937]\n",
      " [ 0.88626486  0.11373514]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.92033791  0.07966209]\n",
      " [ 0.94411699  0.05588301]\n",
      " [ 0.88626486  0.11373514]\n",
      " [ 0.94444569  0.05555431]\n",
      " [ 0.94285015  0.05714985]\n",
      " [ 0.88289533  0.11710467]\n",
      " [ 0.94358933  0.05641067]\n",
      " [ 0.93961695  0.06038305]\n",
      " [ 0.94496063  0.05503937]]\n"
     ]
    }
   ],
   "source": [
    "print(len((forest_y_score)[3]))\n",
    "print((forest_y_score)[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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M1zQwMBA9e/ZEfHw81Gq12E52dra4n56ejoSEBCQmJuLRo0e4efMmbty4AX9/f6uuOHfv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\nnDlzBvb29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AMDA5VtBIDo6GgUFBQgPDy8QefJmDx5MlJTUxEcHIysrCzMnz8fZmZmWLRo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\nq95CGvgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb3d16a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 8. 画图（ROC图）\n",
    "plt.figure(figsize=(8, 6), facecolor='w')\n",
    "plt.plot(forest_fpr1,forest_tpr1,c='r',lw=2,label=u'Hinselmann目标属性,AUC=%.3f' % auc1)\n",
    "plt.plot(forest_fpr2,forest_tpr2,c='b',lw=2,label=u'Schiller目标属性,AUC=%.3f' % auc2)\n",
    "plt.plot(forest_fpr3,forest_tpr3,c='g',lw=2,label=u'Citology目标属性,AUC=%.3f' % auc3)\n",
    "plt.plot(forest_fpr4,forest_tpr4,c='y',lw=2,label=u'Biopsy目标属性,AUC=%.3f' % auc4)\n",
    "plt.plot((0,1),(0,1),c='#a0a0a0',lw=2,ls='--')\n",
    "plt.xlim(-0.001, 1.001)\n",
    "plt.ylim(-0.001, 1.001)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "plt.xlabel('False Positive Rate(FPR)', fontsize=16)\n",
    "plt.ylabel('True Positive Rate(TPR)', fontsize=16)\n",
    "plt.grid(b=True, ls=':')\n",
    "plt.legend(loc='lower right', fancybox=True, framealpha=0.8, fontsize=12)\n",
    "plt.title(u'随机森林多目标属性分类ROC曲线', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练样本数量429，测试样本数量:429\n",
      "1决策树数目，1最大深度，正确率:86.48%\n",
      "1决策树数目，2最大深度，正确率:86.95%\n",
      "1决策树数目，3最大深度，正确率:84.62%\n",
      "1决策树数目，7最大深度，正确率:82.75%\n",
      "1决策树数目，15最大深度，正确率:78.09%\n",
      "50决策树数目，1最大深度，正确率:86.71%\n",
      "50决策树数目，2最大深度，正确率:86.48%\n",
      "50决策树数目，3最大深度，正确率:86.48%\n",
      "50决策树数目，7最大深度，正确率:86.25%\n",
      "50决策树数目，15最大深度，正确率:84.38%\n",
      "100决策树数目，1最大深度，正确率:86.95%\n",
      "100决策树数目，2最大深度，正确率:86.25%\n",
      "100决策树数目，3最大深度，正确率:86.48%\n",
      "100决策树数目，7最大深度，正确率:86.25%\n",
      "100决策树数目，15最大深度，正确率:85.08%\n",
      "500决策树数目，1最大深度，正确率:86.48%\n",
      "500决策树数目，2最大深度，正确率:86.48%\n",
      "500决策树数目，3最大深度，正确率:86.48%\n",
      "500决策树数目，7最大深度，正确率:86.25%\n",
      "500决策树数目，15最大深度，正确率:84.85%\n"
     ]
    }
   ],
   "source": [
    "#比较不同树数目、树最大深度的情况下随机森林的正确率\n",
    "#一般情况下，初始的随机森林树个数是100，深度1，如果需要我们再进行优化操作\n",
    "x_train2,x_test2,y_train2,y_test2 = train_test_split(X, Y, test_size=0.5, random_state=0)\n",
    "print (\"训练样本数量%d，测试样本数量:%d\" % (x_train2.shape[0], x_test2.shape[0]))\n",
    "## 比较\n",
    "estimators = [1,50,100,500]\n",
    "depth = [1,2,3,7,15]\n",
    "x1, x2 = np.meshgrid(estimators, depth)\n",
    "err_list = []\n",
    "for es in estimators:\n",
    "    es_list = []\n",
    "    for d in depth:\n",
    "        tf = RandomForestClassifier(n_estimators=es, criterion='gini', max_depth=d, max_features = None, random_state=0)\n",
    "        tf.fit(x_train2, y_train2)\n",
    "        st = tf.score(x_test2, y_test2)\n",
    "        err = 1 - st\n",
    "        es_list.append(err)\n",
    "        print (\"%d决策树数目，%d最大深度，正确率:%.2f%%\" % (es, d, st * 100))\n",
    "    err_list.append(es_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VY32enp7EcRwdOHBAZf+mTZuIMUZTpkxRK3P27FlijNFvv/2Wp7ExxqhVq1Yaj02bNo04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5SzkA0TIzd27FhDl2AUCi0HjQa2boU334Ru3ZSVfrduVW7jDBsGlpaFc9x8kOeEQuagI7NQyBx0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\nIUQ0KDEhhBBCiGhQYkIIIYQQ0fh/7gklol36ZxIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb82ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 画图\n",
    "plt.figure(facecolor='w')\n",
    "i = 0\n",
    "colors = ['r','b','g','y']\n",
    "lw = [1,2,4,3]\n",
    "max_err = 0\n",
    "min_err = 100\n",
    "for es,l in zip(estimators,err_list):\n",
    "    plt.plot(depth, l, c=colors[i], lw=lw[i], label=u'树数目:%d' % es)\n",
    "    max_err = max((max(l),max_err))\n",
    "    min_err = min((min(l),min_err))\n",
    "    i += 1\n",
    "plt.xlabel(u'树深度', fontsize=16)\n",
    "plt.ylabel(u'错误率', fontsize=16)\n",
    "plt.legend(loc='upper left', fancybox=True, framealpha=0.8, fontsize=12)\n",
    "plt.grid(True)\n",
    "plt.xlim(min(depth),max(depth))\n",
    "plt.ylim(min_err * 0.99, max_err * 1.01)\n",
    "plt.title(u'随机森林中树数目、深度和错误率的关系图', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# \n",
    "1、了解缺省值处理方法\n",
    "2、了解随机深林API的使用\n",
    "3、回顾ROC曲线的计算\n",
    "4、了解一般情况下，随机森林的树的个数是100，深度是1\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
